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Understanding Ontologies: Definitions, Applications, and Importance in AI

This comprehensive overview explores the concept of ontologies, defined as the study of existence and the structure of reality. Drawing on definitions from notable thinkers like Tom Gruber and John F. Sowa, it examines how ontologies serve as formal systems for representing knowledge, including their role in artificial intelligence. The text distinguishes between formal and informal ontologies, outlining their specifications and applications, particularly regarding knowledge interchange and information systems. Discover how ontologies enhance communication and reduce information loss across domains.

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Understanding Ontologies: Definitions, Applications, and Importance in AI

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Presentation Transcript

  1. Onto – What? By Cartic Ramakrishnan

  2. What are ontologies?

  3. The Oxford English dictionary says Ontologies are: • a branch of metaphysics • the science of being • the nature of existence and the categorical structure of reality

  4. Tom Gruber’s definition • An ontology is an explicit specification of a conceptualization. • The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. • For Artificial Intelligence systems what exists is that which can be represented. • When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. • Those set of objects, and the describable relationship among them, are reflected in the representational vocabulary with which a knowledge-base program represents knowledge.

  5. John F. Sowa’s definition • An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. • A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation. • Formal ontologies are further distinguished by the way the subtypes are distinguished from their super-types: • Axiomatized ontology • Prototype-based ontology

  6. Jeff Heflin’s formal definition of an Ontology • Given a logical language L, an ontology is a tuple <V, A>, where thevocabulary VSpis some subset of the predicate symbols of Land the axioms AWare a subset of the well-formed formulas of L.

  7. Gruber – Ontological Commitments • We use common ontologies to describe ontological commitments for a set of agents so that they can communicate about a domain of discourse without necessarily operating on a globally shared theory. • We say that an agent commits to an ontology if its observable actions are consistent with the definitions in the ontology.

  8. Knowledge Interchange Format Standard Ontologies

  9. Algebra Ontology (defrelation binop (?f ?s) := (and (binary-function ?f) (subset (universe ?f) ?s))) (defrelation associative (?f ?s) := (forall (?x ?y ?z) (=> (member ?x ?s) (member ?y ?s) (member ?z ?s) (= (value ?f ?x (value ?f ?y ?z)) (value ?f (value ?f ?x ?y) ?z)))))

  10. Ontologies Vs. KBS

  11. The database perspective

  12. Past trends in database approaches • Inspired by needs and methods for RDB systems • Relational schemas are purely lexical • Obtained from conceptual schemas “flattened” into tables • Information loss relating to roles and concepts

  13. Ontology based approach • Does not require the “flattening” into a single relation • More consensual view of knowledge • Reduces information loss

  14. “World” Conceptual Schema Using Ontology for Semantics designer interpretation Any Design Tool domain expert Implementation ONTOLOGY agreement Information System (including the WWW) user Data

  15. DOGMA • an ontology server in order to assist the gathering and incremental growth of ontologies • achieve at least a degree of scalability not possible with more complex representations • extensional approach

  16. Addendum - Examples

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